A Computational Toolchain for the Automatic Generation of Multiple Reduced-Order Models from CFD Simulations

Thibault Marzullo; Marcus M. Keane; Marco Geron; Rory F.D. Monaghan

A Computational Toolchain for the Automatic Generation of Multiple Reduced-Order Models from CFD Simulations

Thibault Marzullo, Marcus M. Keane, Marco Geron, Rory F.D. Monaghan

School of Mechanical and Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

The present study aims to develop a systematic tool chain for automatically extracting accurate Reduced-Order Models (ROMs) from Computational Fluid Dynamics (CFD) simulations for use in the design and operation of near-zero energy buildings, with a higher accuracy than traditional zonal models but at a fraction of the computational cost of CFD. This study assesses the accuracy and time to solution of ROMs when solved for different Boundary Conditions (BCs) in order to define the usability envelope of the automatically extracted ROMs. The parameters used in this study are inlet temperatures and mass flow rates. Results show that the absolute error can be kept under 0.5K for changes in temperature of up to ±15K and under 0.25K for changes in mass flow rates of up to ±45% of the original value. The results show that this method has potential for applications in the built environment where its accuracy and low computational cost can bridge a gap between low order RC models and high order CFD, further improving energy efficiency in smart buildings.

Original language	English
Title of host publication	13th Conference on Sustainable Development of Energy, Water and Environment Systems
Number of pages	12
Publication status	Published - Oct 2018
Event	13th conference on Sustainable Development of Energy,Water and Environment Systems (SDEWES) - Palermo, Palermo, Italy Duration: 30 Sep 2018 → 04 Oct 2018 http://www.palermo2018.sdewes.org/

Conference

Conference	13th conference on Sustainable Development of Energy,Water and Environment Systems (SDEWES)
Country	Italy
City	Palermo
Period	30/09/2018 → 04/10/2018
Internet address	http://www.palermo2018.sdewes.org/

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Cite this

@inproceedings{a83202db953a4306a0f837026e0aa219,

title = "A Computational Toolchain for the Automatic Generation of Multiple Reduced-Order Models from CFD Simulations",

abstract = "The present study aims to develop a systematic tool chain for automatically extracting accurate Reduced-Order Models (ROMs) from Computational Fluid Dynamics (CFD) simulations for use in the design and operation of near-zero energy buildings, with a higher accuracy than traditional zonal models but at a fraction of the computational cost of CFD. This study assesses the accuracy and time to solution of ROMs when solved for different Boundary Conditions (BCs) in order to define the usability envelope of the automatically extracted ROMs. The parameters used in this study are inlet temperatures and mass flow rates. Results show that the absolute error can be kept under 0.5K for changes in temperature of up to ±15K and under 0.25K for changes in mass flow rates of up to ±45% of the original value. The results show that this method has potential for applications in the built environment where its accuracy and low computational cost can bridge a gap between low order RC models and high order CFD, further improving energy efficiency in smart buildings.",

author = "Thibault Marzullo and Keane, {Marcus M.} and Marco Geron and Monaghan, {Rory F.D.}",

year = "2018",

month = oct,

language = "English",

booktitle = "13th Conference on Sustainable Development of Energy, Water and Environment Systems",

note = "13th conference on Sustainable Development of Energy,Water and Environment Systems (SDEWES) ; Conference date: 30-09-2018 Through 04-10-2018",

url = "http://www.palermo2018.sdewes.org/",

}

Marzullo, T, Keane, MM, Geron, M & Monaghan, RFD 2018, A Computational Toolchain for the Automatic Generation of Multiple Reduced-Order Models from CFD Simulations. in 13th Conference on Sustainable Development of Energy, Water and Environment Systems. 13th conference on Sustainable Development of Energy,Water and Environment Systems (SDEWES), Palermo, Italy, 30/09/2018.

TY - GEN

T1 - A Computational Toolchain for the Automatic Generation of Multiple Reduced-Order Models from CFD Simulations

AU - Marzullo, Thibault

AU - Keane, Marcus M.

AU - Geron, Marco

AU - Monaghan, Rory F.D.

PY - 2018/10

Y1 - 2018/10

N2 - The present study aims to develop a systematic tool chain for automatically extracting accurate Reduced-Order Models (ROMs) from Computational Fluid Dynamics (CFD) simulations for use in the design and operation of near-zero energy buildings, with a higher accuracy than traditional zonal models but at a fraction of the computational cost of CFD. This study assesses the accuracy and time to solution of ROMs when solved for different Boundary Conditions (BCs) in order to define the usability envelope of the automatically extracted ROMs. The parameters used in this study are inlet temperatures and mass flow rates. Results show that the absolute error can be kept under 0.5K for changes in temperature of up to ±15K and under 0.25K for changes in mass flow rates of up to ±45% of the original value. The results show that this method has potential for applications in the built environment where its accuracy and low computational cost can bridge a gap between low order RC models and high order CFD, further improving energy efficiency in smart buildings.

AB - The present study aims to develop a systematic tool chain for automatically extracting accurate Reduced-Order Models (ROMs) from Computational Fluid Dynamics (CFD) simulations for use in the design and operation of near-zero energy buildings, with a higher accuracy than traditional zonal models but at a fraction of the computational cost of CFD. This study assesses the accuracy and time to solution of ROMs when solved for different Boundary Conditions (BCs) in order to define the usability envelope of the automatically extracted ROMs. The parameters used in this study are inlet temperatures and mass flow rates. Results show that the absolute error can be kept under 0.5K for changes in temperature of up to ±15K and under 0.25K for changes in mass flow rates of up to ±45% of the original value. The results show that this method has potential for applications in the built environment where its accuracy and low computational cost can bridge a gap between low order RC models and high order CFD, further improving energy efficiency in smart buildings.

M3 - Conference contribution

BT - 13th Conference on Sustainable Development of Energy, Water and Environment Systems

T2 - 13th conference on Sustainable Development of Energy,Water and Environment Systems (SDEWES)

Y2 - 30 September 2018 through 4 October 2018

ER -